polars-nlq 0.3.0

polars-nlq

polars-nlq turns natural language questions into a typed Polars query plan, then executes that plan against a DataFrame or LazyFrame.

The library is built around two functions:

• nl_query(...) to generate a validated Plan with instructor
• execute_plan(...) to execute that Plan with Polars

Warning: plans created by LLMs can be incorrect and should be reviewed by a human before use.

Install

pip install polars-nlq

Quick Example

import instructor
import polars as pl
from openai import OpenAI

from polars_nlq import execute_plan, nl_query

# Columns: name,city,sales
q1 = pl.scan_csv("sales.csv")

# Local OpenAI-compatible endpoint
openai_client = OpenAI(base_url="http://localhost:8080/v1", api_key="dummy")
client = instructor.from_openai(openai_client)

plan = nl_query(client, q1.collect_schema(), "sum of sales by city, with at least 20 sales")

results_lf = execute_plan(q1, plan)  # execute_plan always returns a LazyFrame

print(results_lf.collect())

Plan Model

Plans are typed with Pydantic models and validated before execution.

• Expressions: col, lit, unary, binary, func, when_then_otherwise
• Operations: select, with_columns, filter, groupby_agg, sort, limit

This gives you a clear contract between LLM output and execution, and plans can be serialized and reused.

Example plan from the query above:

ops=[GroupByAgg(op='groupby_agg', by=[Col(kind='col', name='city')], maintain_order=False, named_by={}, aggs=[NamedExpr(expr=Func(kind='func', name='sum', args=[Col(kind='col', name='sales')]), alias='total_sales')], named_aggs={}), Filter(op='filter', predicate=Binary(kind='binary', op=<BinaryOp.GTE: 'gte'>, left=Col(kind='col', name='total_sales'), right=Lit(kind='lit', value=20))), Select(op='select', exprs=[NamedExpr(expr=Col(kind='col', name='city'), alias=None), NamedExpr(expr=Col(kind='col', name='total_sales'), alias=None)])]

API

nl_query(client, schema, question, model="local-model") -> Plan

• client: instructor-wrapped client that supports chat.completions.create
• schema: mapping-like schema (for example LazyFrame.collect_schema())
• question: natural language prompt
• model: model name passed to chat.completions.create (defaults to "local-model")

Returns a validated Plan.

execute_plan(source, plan) -> pl.LazyFrame

• source: pl.DataFrame or pl.LazyFrame
• plan: Plan instance or compatible dict

Returns collected query results as a pl.LazyFrame.

Run Tests

uv run pytest -q

Limitations

• Derived columns are not supported in plans (for example, grouping by year from a date column).
• Joins are not supported.